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Artificial Intelligence: a Disruptive Tool for a Smarter Medicine

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Artificial Intelligence: a Disruptive Tool for a Smarter Medicine European Review for Medical and Pharmacological Sciences 2020; 24: 7462-7474 Artificial intelligence: a disruptive tool for a smarter medicine C.M. GALMARINI, M. LUCIUS Topazium Artificial Intelligence, Madrid, Spain Abstract. – OBJECTIVE: Although highly dinary: in just 100 years, life expectancy has successful, the medical R&D model is failing rocketed from approximately 45 to 72 years. at improving people’s health due to a series of However, there is still a lot of work to be done flaws and defects inherent to the model itself. since the figure for all-cause mortality stands at A new collective intelligence, incorporating hu- almost 57 million. This figure includes 47 mil- man and artificial intelligence (AI) could over- come these obstacles. Because AI will play a lions of adults with chronic, non-communicable key role in this new collective intelligence, it diseases, and 6.5 millions of children, of whom is necessary that those involved in healthcare 5.6 million are under the age of five1. In spite of have a general knowledge of how these technol- the massive economic efforts in R&D and inno- ogies work. With this comprehensive review, we vation, global health improvement seems to have intend to provide it. MATERIALS AND METHODS: reached a plateau. The current model of medical A broad-rang- research is showing difficulties due to a series of ing search has been undertaken on institutional 2 and non-institutional websites in order to identi- limitations . Firstly, its current framework mir- fy relevant papers, comments and reports. rors a linear and sequential process, which has RESULTS: We firstly describe the flaws and been the only paradigm accepted so far. In this defects of the current R&D biomedical model linear approach, investment in R&D produces and how the generation of a new collective in- results that are, at best, only proportional to the telligence will result in a better and wiser med- level of investment. Furthermore, in the current icine through a truly personalized and holistic approach. We, then, discuss the new forms of R&D model the outcome information is usually data collection and data processing and the dif- stored in closed and sealed compartments with ferent types of artificial learning and their spe- very limited access to outsiders, sometimes even cific algorithms. Finally, we review the current within companies or institutions. These “Chinese uses and applications of AI in the biomedical walls” not only prevent innovation, they also in- field and how these can be expanded, as well as crease the time and cost of delivering new drugs the limitations and challenges of applying these or medical devices to patients. Additionally, the new technologies in the medical field. CONCLUSIONS: This colossal common effort current model assesses “value” as the amount of based on a new collective intelligence will ex- “assets” an organization owns (patents, chemical ponentially improve the quality of medical re- entities, etc.) and the relevance of an organization search, resulting in a radical change for the bet- in the market is measured based on these assets. ter in the healthcare model. AI, without replac- However, the paucity of data encourages monop- ing us, is here to help us achieve the ambitious olization of resources and discourages the sharing goal set by the WHO in the Alma Ata declaration of 1978: “Health for All”. of information, which in turn hinders innovation. Thus, the current model drives pharmaceutical Key Words: and biotechnology industries to generate innova- Artificial intelligence, Computational intelligence, Computer vision systems, Biomedical research, On- tion and value through the acquisition of assets cology. and discoveries from other stakeholders, which results in overcrowding of companies within this sector. All these hurdles have led to a R&D model Introduction that no longer works as fast as wanted. Around 300 billion US dollars are invested A New Medical R&D Model Is Needed every year in medical research worldwide. The Investing more financial and human resources result of this collective effort has been extraor- is not enough to modify the current R&D model 7462 Corresponding Author: Carlos M. Galmarini MD, Ph.D; e-mail: [email protected] Artificial intelligence: a disruptive tool for a smarter medicine in a way that would allow to achieve the ultimate “intelligently”4. Top applications in healthcare goal of improving healthcare. A radical change is include drug discovery and development, auto- needed. This should be based on a new form of mated devices, wearables, diagnostic and medical collective intelligence able to overcome the ob- imaging devices, remote monitoring of patients, stacles of the current model. Since it appears that predictive medicine, robotics and management human intelligence is not enough, this new type of healthcare systems6. There are two key factors of collective intelligence should include artificial that allowed progress in the development of AI intelligence (AI)3. We can use this new kind of (Figure 1). Firstly, in recent years, new sources of collective intelligence to create smarter organiza- medical data, such as health monitoring devices, tions, conduct smarter research and solve medical mobile apps or electronic health records (EHRs) problems that, up to now, where thought to have have been developed, leading to the generation no solution4. How and why will AI revolutionize of vast data banks containing patient data. A medical research? It is known that when a process second factor is the access to new analytical tools becomes digitized and powered by information that can mine and process vast amounts of data. flow, its rate of development jumps onto an ex- This allows to perform pattern recognition, data ponential growth path, doubling every two years classification and predictions in a much shorter (Moore’s Law). Indeed, this system encourages in period of time. silico research, which exponentially reduces the research costs and promotes innovation, result- Data Access ing in a wealth of information and a further cost The success of AI relies on its ability to access reduction, thus generating a positive feedback the right kind of data in an appropriate volume. loop where the output dramatically increases, This represents an enormous challenge. It is cal- since all unnecessary processes and expenditure culated that approximately a zettabyte (a trillion on purposes other than the generation of val- gigabytes) of medical data is generated each year. ue have been eliminated. This information-led Due to the existence of different technologies that environment represents a paradigm shift, as it allow to collect data more easily, this amount is promotes knowledge sharing and the discovery of expected to double every two years7,8. innovative solutions. In this new model, the most benefited are those who share more knowledge New Forms of Data Collection and acquire new knowledge faster. Wearables can deliver an unprecedented amount However, AI elicits many fears, as it is thought of data from millions of people. For example, smart- that it has the potential to replace human intelli- phones are one of the most powerful devices for gence. Yet these fears are unfounded, for AI should health monitoring. Indeed, these regular devices be viewed as a tool rather than a replacement. contain sensors, as well as processing and commu- Therefore, what can AI add to human intelligence? nication capabilities that allow data to be collected, Nowadays, we are overwhelmed with information analysed, and shared. Collecting daily measure- from different new sources (e.g., mobile phone ments with a smartphone may reveal nuances that technology, wearable devices, etc.). AI can integrate are not evident in monthly clinic visits. Similarly, these vast amounts of data and identify relevant pat- in recent years, increased attention has been giv- terns in a way that a human mind would be unable en to patient-reported outcomes (PROs), which to do. On the other hand, AI cannot replace human provide a direct indicator of a patient’s health judgment and wisdom. Therefore, human and ma- condition without correction or interpretation by chine intelligence can complement each other5. Be- a clinician9-12. These data are complementary cause AI will play a key role in medical research, it to those collected by the doctor as they provide is necessary that those involved in this field have a additional information on other aspects that are general knowledge of how these technologies work. important to the patient and that, perhaps, clini- Thus, the aim of this review is to offer a comprehen- cians do not expect or take into account. PROs sive background on AI, as well as its current uses thus are essential components in high-quality, and applications in the medical field, and how these patient-centred care13,14. Collection and integra- can be expanded in the future. tion of ePRO into routine care is feasible, as demonstrated by different studies showing how it Almost Human: Artificial Intelligence increases clinician awareness, resulting in an ear- AI is the field of computer science dealing ly response to patient symptoms and ultimately, a with the development of computers that can act better quality of life14,15. 7463 C.M. Galmarini, M. Lucius Figure 1. Overview of the AI workflow. The first step is data collection. Data can be collected from different devices and data- bases generated by universities, hospitals, and biotech and pharmaceutical companies. The second step comprises data processing and data modelling, including feature engineering, model training, evaluation and validation. During this stage, the system can process vast amounts of data and deliver an actionable output that can be a pattern, a classification and/or a prediction. Data Processing al hurdle: patient data must be anonymized and The completeness and quality of the collected pooled. New developments, such as blockchain data will determine the actual usefulness of the could be used to build data systems that allow database, since the performance of any model re- people to easily and securely share their health lies on the data used to train it16.
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